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Objective Mapping of Terrestrial and Planetary Surface Features: Remote Sensing...
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Objective Mapping of Terrestrial and Planetary Surface Features: Remote Sensing in Geosciences

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 15 May 2025 | Viewed by 2561

Special Issue Editor

Prof. Dr. Alberto González-Díez

E-Mail Website
Guest Editor
Departamento de Ciencias de la Tierra y Física de la Materia Condensada (Earth Science and Condensed Matter Physics Department), Universidad de Cantabria, Cantabria, Spain
Interests: the objective mapping of surface features using filtering techniques based on MDE or other images; climate change; spatial analysis; engineering geology; geological mapping; slope stability; digital mapping; satellite image analysis; geospatial science; geological processes; satellite image processing

Special Issue Information

Dear Colleagues,

The reduction in subjectivity in mapping the surface features of reality has been of interest to the scientific community since the last decade of the 20th century. For the Earth or any other planet, remote sensing tools greatly improve the objective mapping of geomorphic features on surfaces. The development of objective procedures for mapping geomorphic features is key to the design of robotic sensors for analyzing the existing surface reality on planets.

Using aerial or terrestrial laser scanning, digital photogrammetry, or any other measurement technique, it is possible to obtain high-spatial-resolution digital elevation models (HRDEMs). These methods, duly treated by remote sensing and image processing techniques, allow researchers to obtain vectorial contacts or feature geomorphic references (FRGs), the latter of which provide an accurate representation of the reality of and the objects present on the studied surface. Other new procedures also provide similar solutions.

The analysis of images captured using sensors (LiDAR, Radar, spectral sensors, traditional imaging cameras, CT scans, etc.) mounted on aircraft (planes, drones, or space platforms) or integrated into short-range devices plays a crucial role in achieving these objectives. Whether processed with remote sensing and GIS tools, image filtering algorithms, or any 3D application, as well as new methodological approaches, these images contribute significantly to generating surface models. These models are essential for carrying out an objective representation of natural features.

This SI aims to compile the latest research on remote sensing applications for the objective mapping of surface features. This SI invites papers on the aforementioned topics related to the objective mapping of natural surface features. This includes the construction of inventories detailing the geometrical characteristics of these geomorphic features and the analysis of their dynamics through these features. This extends to features associated with external or internal geological processes, trace fossils, atmospheric gases, and any feature studied in geoscience that can be automatically examined by robotic sensors in the near future.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

We invite you to submit scientific, technological, or review articles about recent research within one or more of these topics:

  • The objective mapping of geomorphic features for geoscience studies.
  • Making realistic inventories of surface features on planetary bodies.
  • Detection and measures of earth surface changes using multitemporal remote sensing signals.
  • Mapping, modeling, and/or monitoring approaches in earth surface changes and deformations.
  • Automatic landmark extraction to the geometric morphometrics analyses of body fossil remains.

Prof. Dr. Alberto González-Díez
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • precise DTM or DSM
  • image filtering techniques
  • modelling in GIS and remote sensing environments
  • interferometric measurements
  • LiDAR cloud of points
  • digital photogrammetric models
  • precise measurements of surface deformation
  • inventories of planetary geoforms
  • landmark extraction
  • physics of the earth
  • tectonics
  • engineering geology
  • geomorphology
  • paleontology
  • archaeology

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

23 pages, 26242 KiB  
Article
The Application of Fast Fourier Transform Filtering to High Spatial Resolution Digital Terrain Models Derived from LiDAR Sensors for the Objective Mapping of Surface Features and Digital Terrain Model Evaluations
by Alberto González-Díez, Ignacio Díaz-Martínez, Pablo Cruz-Hernández, Antonio Barreda-Argüeso and Matthew Doughty
Remote Sens. 2025, 17(1), 150; https://doi.org/10.3390/rs17010150 - 4 Jan 2025
Viewed by 334
Abstract
In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability [...] Read more.
In this paper, the application is investigated of fast Fourier transform filtering (FFT-FR) to high spatial resolution digital terrain models (HR-DTM) derived from LiDAR sensors, assessing its efficacy in identifying genuine relief elements, including both natural geological features and anthropogenic landforms. The suitability of the derived filtered geomorphic references (FGRs) is evaluated through spatial correlation with ground truths (GTs) extracted from the topographical and geological geodatabases of Santander Bay, Northern Spain. In this study, it is revealed that existing artefacts, derived from vegetation or human infrastructures, pose challenges in the units’ construction, and large physiographic units are better represented using low-pass filters, whereas detailed units are more accurately depicted with high-pass filters. The results indicate a propensity of high-frequency filters to detect anthropogenic elements within the DTM. The quality of GTs used for validation proves more critical than the geodatabase scale. Additionally, in this study, it is demonstrated that the footprint of buildings remains uneliminated, indicating that the model is a poorly refined digital surface model (DSM) rather than a true digital terrain model (DTM). Experiments validate the DTM’s capability to highlight contacts and constructions, with water detection showing high precision (≥60%) and varying precision for buildings. Large units are better captured with low filters, whilst high filters effectively detect anthropogenic elements and more detailed units. This facilitates the design of validation and correction procedures for DEMs derived from LiDAR point clouds, enhancing the potential for more accurate and objective Earth surface representation. Full article
(This article belongs to the Special Issue Objective Mapping of Terrestrial and Planetary Surface Features: Remote Sensing in Geosciences)
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23 pages, 11423 KiB  
Article
Kernel Density Estimation for the Interpretation of Seismic Big Data in Tectonics Using QGIS: The Türkiye–Syria Earthquakes (2023)
by David Amador Luna, Francisco M. Alonso-Chaves and Carlos Fernández
Remote Sens. 2024, 16(20), 3849; https://doi.org/10.3390/rs16203849 - 16 Oct 2024
Viewed by 845
Abstract
Numerous studies have utilized remote sensing techniques to analyze seismic data in active areas. Point density techniques, widely used in remote sensing, examine the spatial distribution of point clouds related to specific variables. Applying these techniques to complex tectonic settings, such as the [...] Read more.
Numerous studies have utilized remote sensing techniques to analyze seismic data in active areas. Point density techniques, widely used in remote sensing, examine the spatial distribution of point clouds related to specific variables. Applying these techniques to complex tectonic settings, such as the East Anatolian Fault Zone, helps identify major active fractures using both surface and deep information. This study employed kernel density estimation (KDE) to compare two distinct point-cloud populations from the seismic event along the Türkiye–Syria border on 6 February 2023, providing insights into the main active orientations supporting the Global Tectonics framework. This study considered two populations of seismic foci point clouds containing over 40,000 events, recorded by the Turkish Disaster and Emergency Management Authority (AFAD) and Kandilli Observatory and Earthquake Research Institute (KOERI). These populations were divided into two datasets: crude and relocated-filtered. Kernel density analysis demonstrated that both datasets yielded similar geological interpretations. The high-density cores of both datasets perfectly matched, exhibiting identical structures consistent with geological knowledge. Areas with a minimal concentration of earthquakes at depth were also identified, separating different crustal strength levels. Full article
(This article belongs to the Special Issue Objective Mapping of Terrestrial and Planetary Surface Features: Remote Sensing in Geosciences)
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22 pages, 9512 KiB  
Article
Neural Network-Based Fusion of InSAR and Optical Digital Elevation Models with Consideration of Local Terrain Features
by Rong Gui, Yuanjun Qin, Zhi Hu, Jiazhen Dong, Qian Sun, Jun Hu, Yibo Yuan and Zhiwei Mo
Remote Sens. 2024, 16(19), 3567; https://doi.org/10.3390/rs16193567 - 25 Sep 2024
Viewed by 769
Abstract
InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated [...] Read more.
InSAR and optical techniques represent two principal approaches for the generation of large-scale Digital Elevation Models (DEMs). Due to the inherent limitations of each technology, a single data source is insufficient to produce high-quality DEM products. The increasing deployment of satellites has generated vast amounts of InSAR and optical DEM data, thereby providing opportunities to enhance the quality of final DEM products through the more effective utilization of the existing data. Previous research has established that complete DEMs generated by InSAR technology can be combined with optical DEMs to produce a fused DEM with enhanced accuracy and reduced noise. Traditional DEM fusion methods typically employ weighted averaging to compute the fusion results. Theoretically, if the weights are appropriately selected, the fusion outcome can be optimized. However, in practical scenarios, DEMs frequently lack prior information on weights, particularly precise weight data. To address this issue, this study adopts a fully connected artificial neural network for elevation fusion prediction. This approach represents an advancement over existing neural network models by integrating local elevation and terrain as input features and incorporating curvature as an additional terrain characteristic to enhance the representation of terrain features. We also investigate the impact of terrain factors and local terrain feature as training features on the fused elevation outputs. Finally, three representative study areas located in Oregon, USA, and Macao, China, were selected for empirical validation. The terrain data comprise InSAR DEM, AW3D30 DEM, and Lidar DEM. The results indicate that compared to traditional neural network methods, the proposed approach improves the Root-Mean-Squared Error (RMSE) ranges, from 5.0% to 12.3%, and the Normalized Median Absolute Deviation (NMAD) ranges, from 10.3% to 26.6%, in the test areas, thereby validating the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Objective Mapping of Terrestrial and Planetary Surface Features: Remote Sensing in Geosciences)
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